Triple
T15934452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Everglazed Donuts & Cold Brew |
E386404
|
entity |
| Predicate | primaryBeverageType |
P68535
|
FINISHED |
| Object | cold brew coffee |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: cold brew coffee | Statement: [Everglazed Donuts & Cold Brew, primaryBeverageType, cold brew coffee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryBeverageType Context triple: [Everglazed Donuts & Cold Brew, primaryBeverageType, cold brew coffee]
-
A.
beverageSubcategory
chosen
Indicates a more specific classification within a broader beverage category, defining the subtype or subcategory of a drink.
-
B.
traditionalDrink
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
-
C.
favoriteDrink
Indicates that one entity has a preferred beverage over others.
-
D.
drinks
Indicates that one entity consumes a liquid substance, typically by ingesting it through the mouth.
-
E.
alcoholType
Indicates the specific kind or category of alcohol associated with an entity (e.g., beer, wine, spirits).
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4d08f481909f38b75e3f42d9ab |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d37cd88190ab50760f1783e20c |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:53 a.m.